FPGA-Based Anomalous Trajectory Detection Using SOFM
ARC '09 Proceedings of the 5th International Workshop on Reconfigurable Computing: Architectures, Tools and Applications
Trajectory-based handball video understanding
Proceedings of the ACM International Conference on Image and Video Retrieval
Object tracking using multiple fragments
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Anomalous video event detection using spatiotemporal context
Computer Vision and Image Understanding
Clustering of trajectories in video surveillance using growing neural gas
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Annotated free-hand sketches for video retrieval using object semantics and motion
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Towards a quantitative approach for comparing crowds
Computer Animation and Virtual Worlds
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
Learning motion patterns in unstructured scene based on latent structural information
Journal of Visual Languages and Computing
International Journal of Computer Vision
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In this paper, we propose a framework for event detection based on trajectory clustering and 4-D histograms. In the training period, captured trajectories are grouped into coherent clusters according to global motion flows. Within each cluster, the position and instantaneous velocity of each tracked object are used to build a 4-D motion histogram for the cluster. In the test period, each new trajectory is compared against the 4-D histograms of all clusters, so that its coherence with previously tracked objects can be evaluated. Experimental results showed that these criteria can be effectively used to measure the coherence of test trajectories with those in the training stage, allowing a range of events to be detected in surveillance and traffic applications.